A hybrid of sequential rules and collaborative filtering for product recommendation

Duen-Ren Liu; Chin Hui Lai; Wang Jung Lee

doi:10.1109/CEC-EEE.2007.6

A hybrid of sequential rules and collaborative filtering for product recommendation

Duen-Ren Liu^*, Chin Hui Lai, Wang Jung Lee

^*Corresponding author for this work

Institute of Information Management

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

Customers' purchase behavior may vary over time. Traditional collaborative filtering (CF) methods make recommendations to a target customer based on the purchase behavior of customers whose preferences are similar to those of the target customer; however, the methods do not consider how the customers' purchase behavior may vary over time. Although the sequential rule method considers the sequence of customers' purchase behavior over time, it does not make use of the target customer's purchase data for the current period. To resolve the above problems, this work proposes a novel hybrid recommendation method that combines the segmentation-based sequential rule method with the segmentation-based CF method. Experiment results show that the hybrid method outperforms traditional CF methods.

Original language	English
Title of host publication	Proceedings - The 9th IEEE International Conference on E-Commerce Technology; The 4th IEEE International Conference on Enterprise Computing, E-Commerce and E-Services, CEC/EEE 2007
Pages	211-217
Number of pages	7
DOIs	https://doi.org/10.1109/CEC-EEE.2007.6
State	Published - 2007
Event	9th IEEE International Conference on E-Commerce Technology; The 4th IEEE International Conference on Enterprise Computing, E-Commerce and E-Services, CEC/EEE 2007 - Tokyo, Japan Duration: 23 Jul 2007 → 26 Jul 2007

Publication series

Name	Proceedings - The 9th IEEE International Conference on E-Commerce Technology; The 4th IEEE International Conference on Enterprise Computing, E-Commerce and E-Services, CEC/EEE 2007

Conference

Conference	9th IEEE International Conference on E-Commerce Technology; The 4th IEEE International Conference on Enterprise Computing, E-Commerce and E-Services, CEC/EEE 2007
Country/Territory	Japan
City	Tokyo
Period	23/07/07 → 26/07/07

Keywords

Collaborative filtering
Customer segmentation
Product recommendation
Sequential rule

Access to Document

10.1109/CEC-EEE.2007.6

Cite this

Liu, D.-R., Lai, C. H., & Lee, W. J. (2007). A hybrid of sequential rules and collaborative filtering for product recommendation. In Proceedings - The 9th IEEE International Conference on E-Commerce Technology; The 4th IEEE International Conference on Enterprise Computing, E-Commerce and E-Services, CEC/EEE 2007 (pp. 211-217). Article 4285217 (Proceedings - The 9th IEEE International Conference on E-Commerce Technology; The 4th IEEE International Conference on Enterprise Computing, E-Commerce and E-Services, CEC/EEE 2007). https://doi.org/10.1109/CEC-EEE.2007.6

Liu, Duen-Ren ; Lai, Chin Hui ; Lee, Wang Jung. / A hybrid of sequential rules and collaborative filtering for product recommendation. Proceedings - The 9th IEEE International Conference on E-Commerce Technology; The 4th IEEE International Conference on Enterprise Computing, E-Commerce and E-Services, CEC/EEE 2007. 2007. pp. 211-217 (Proceedings - The 9th IEEE International Conference on E-Commerce Technology; The 4th IEEE International Conference on Enterprise Computing, E-Commerce and E-Services, CEC/EEE 2007).

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title = "A hybrid of sequential rules and collaborative filtering for product recommendation",

abstract = "Customers' purchase behavior may vary over time. Traditional collaborative filtering (CF) methods make recommendations to a target customer based on the purchase behavior of customers whose preferences are similar to those of the target customer; however, the methods do not consider how the customers' purchase behavior may vary over time. Although the sequential rule method considers the sequence of customers' purchase behavior over time, it does not make use of the target customer's purchase data for the current period. To resolve the above problems, this work proposes a novel hybrid recommendation method that combines the segmentation-based sequential rule method with the segmentation-based CF method. Experiment results show that the hybrid method outperforms traditional CF methods.",

keywords = "Collaborative filtering, Customer segmentation, Product recommendation, Sequential rule",

author = "Duen-Ren Liu and Lai, {Chin Hui} and Lee, {Wang Jung}",

year = "2007",

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series = "Proceedings - The 9th IEEE International Conference on E-Commerce Technology; The 4th IEEE International Conference on Enterprise Computing, E-Commerce and E-Services, CEC/EEE 2007",

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booktitle = "Proceedings - The 9th IEEE International Conference on E-Commerce Technology; The 4th IEEE International Conference on Enterprise Computing, E-Commerce and E-Services, CEC/EEE 2007",

note = "9th IEEE International Conference on E-Commerce Technology; The 4th IEEE International Conference on Enterprise Computing, E-Commerce and E-Services, CEC/EEE 2007 ; Conference date: 23-07-2007 Through 26-07-2007",

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Liu, D-R, Lai, CH & Lee, WJ 2007, A hybrid of sequential rules and collaborative filtering for product recommendation. in Proceedings - The 9th IEEE International Conference on E-Commerce Technology; The 4th IEEE International Conference on Enterprise Computing, E-Commerce and E-Services, CEC/EEE 2007., 4285217, Proceedings - The 9th IEEE International Conference on E-Commerce Technology; The 4th IEEE International Conference on Enterprise Computing, E-Commerce and E-Services, CEC/EEE 2007, pp. 211-217, 9th IEEE International Conference on E-Commerce Technology; The 4th IEEE International Conference on Enterprise Computing, E-Commerce and E-Services, CEC/EEE 2007, Tokyo, Japan, 23/07/07. https://doi.org/10.1109/CEC-EEE.2007.6

A hybrid of sequential rules and collaborative filtering for product recommendation. / Liu, Duen-Ren; Lai, Chin Hui; Lee, Wang Jung.
Proceedings - The 9th IEEE International Conference on E-Commerce Technology; The 4th IEEE International Conference on Enterprise Computing, E-Commerce and E-Services, CEC/EEE 2007. 2007. p. 211-217 4285217 (Proceedings - The 9th IEEE International Conference on E-Commerce Technology; The 4th IEEE International Conference on Enterprise Computing, E-Commerce and E-Services, CEC/EEE 2007).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Lai, Chin Hui

AU - Lee, Wang Jung

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N2 - Customers' purchase behavior may vary over time. Traditional collaborative filtering (CF) methods make recommendations to a target customer based on the purchase behavior of customers whose preferences are similar to those of the target customer; however, the methods do not consider how the customers' purchase behavior may vary over time. Although the sequential rule method considers the sequence of customers' purchase behavior over time, it does not make use of the target customer's purchase data for the current period. To resolve the above problems, this work proposes a novel hybrid recommendation method that combines the segmentation-based sequential rule method with the segmentation-based CF method. Experiment results show that the hybrid method outperforms traditional CF methods.

AB - Customers' purchase behavior may vary over time. Traditional collaborative filtering (CF) methods make recommendations to a target customer based on the purchase behavior of customers whose preferences are similar to those of the target customer; however, the methods do not consider how the customers' purchase behavior may vary over time. Although the sequential rule method considers the sequence of customers' purchase behavior over time, it does not make use of the target customer's purchase data for the current period. To resolve the above problems, this work proposes a novel hybrid recommendation method that combines the segmentation-based sequential rule method with the segmentation-based CF method. Experiment results show that the hybrid method outperforms traditional CF methods.

KW - Collaborative filtering

KW - Customer segmentation

KW - Product recommendation

KW - Sequential rule

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Liu DR, Lai CH, Lee WJ. A hybrid of sequential rules and collaborative filtering for product recommendation. In Proceedings - The 9th IEEE International Conference on E-Commerce Technology; The 4th IEEE International Conference on Enterprise Computing, E-Commerce and E-Services, CEC/EEE 2007. 2007. p. 211-217. 4285217. (Proceedings - The 9th IEEE International Conference on E-Commerce Technology; The 4th IEEE International Conference on Enterprise Computing, E-Commerce and E-Services, CEC/EEE 2007). doi: 10.1109/CEC-EEE.2007.6

A hybrid of sequential rules and collaborative filtering for product recommendation

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Keywords

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